Systems consisting of cooperating mobile agents<\/strong> are often used to perform tasks such as coverage control, surveillance, and environmental\u00a0sampling. The persistent monitoring problem arises when agents must monitor a dynamically changing environment<\/strong> which cannot be fully covered by a\u00a0stationary team of agents (as in the coverage control). A result of the\u00a0exploration process is the eventual discovery of various “points of interest”\u00a0which, once detected, become “targets” or “data sources” which need to be monitored. This setting arises in\u00a0multiple application domains ranging from surveillance, environmental monitoring, and energy management\u00a0down to nano-scale systems tasked to track fluorescent or\u00a0magnetic particles for the study of dynamic processes in bio-molecular systems\u00a0and in nano-medical research.<\/p>\n In contrast to a\u00a0patrolling problem\u00a0where “every” point in a\u00a0mission space must be monitored, the problem we address here involves a “finite number” of targets (typically larger than the number of agents)\u00a0which the agents must cooperatively monitor through periodic visits. We model\u00a0each target as a queue which value increases if\u00a0not being covered\u00a0and decreases if\u00a0being covered by agents. Our objective is to minimize the accumulated target values over all targets within a given time horizon.<\/p>\n In the following\u00a0video, 1 agent is performing a persistent monitoring task over\u00a0a 1D\u00a0mission space.<\/p>\nSimulations<\/h4>\n